Category Archives: malware detection

Will fileless malware push the antivirus industry into oblivion?

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The death of antivirus has been prophesied for years now, but the AV industry is still alive and kicking. SentinelOne, though, believes that in-memory resident attacks, i.e. fileless malware, just might be the thing that pushes it into oblivion. They base their conjecture on the results of the attack detections made through over a million of SentinelOne Endpoint Protection Platform agents, deployed in enterprise environments across the world. These detections are made at the endpoint, … More → Continue reading Will fileless malware push the antivirus industry into oblivion?→

Intel’s CHIPSEC can detect CIA’s OS X rootkit

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As details about CIA’s hacking capabilities and tools are, bit by bit, popping to the surface, companies are trying to offer users some piece of mind. In the wake of WikiLeaks’ release of the CIA document dump, Apple has stated that many of the revealed iOS exploits have already been patched, and the company is constantly working to address any new vulnerabilities. “Our products and software are designed to quickly get security updates into the … More → Continue reading Intel’s CHIPSEC can detect CIA’s OS X rootkit→

Researchers set to work on malware-detecting CPUs

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Adding hardware protections to software ones in order to block the ever increasing onslaught of computer malware seems like a solid idea, and a group of researchers have just been given a $275,000 grant from the National Science Foundation to help them work on a possible solution: malware-detecting CPUs. The group includes Dmitry Ponomarev, professor of computer science Binghamton University, Lei Yu, associate professor of computer science at the same, Nael Abu-Ghazaleh, a professor of … More → Continue reading Researchers set to work on malware-detecting CPUs→

How online learning algorithms can help improve Android malware detection

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A group of researchers from Nanyang Technological University, Singapore, have created a novel solution for large-scale Android malware detection. It’s called DroidOL, and it’s an adaptive and scalable malware detection framework based on online learning. “DroidOL’s achieves superior accuracy through extracting high quality features from inter-procedural control-flow graphs (ICFGs) of apps, which are known to be robust against evasion and obfuscation techniques adopted by malware,” the researchers explained. They used the Weisfeiler-Lehman (WL) graph kernel … More → Continue reading How online learning algorithms can help improve Android malware detection→

How cybercriminals evade detection

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A new report by Damballa highlights not only how cybercriminals can stay under the radar for long periods of time, but also the need for enterprises to reassess existing security tools. “Its’s no small feat to keep up with how cybercriminals operate. Attackers have an incredibly vibrant underground community where they can buy or rent anything from C&C infrastructure to sophisticated exploit kits to bare metal malware,” said Stephen Newman, CTO of Damballa. The transience … More → Continue reading How cybercriminals evade detection→